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// Copyright (C) 2002 Samy Bengio (bengio@idiap.ch)
// and Ronan Collobert (collober@iro.umontreal.ca)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch is free software; you can redistribute it and/or modify
// it under the terms of the GNU General Public License as published by
// the Free Software Foundation; either version 2 of the License, or
// (at your option) any later version.
//
// Torch is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
// GNU General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#include "MLP.h"
namespace Torch {
MLP::MLP(int n_inputs_, int n_hidden_, int n_outputs_)
{
n_inputs = n_inputs_;
n_hidden = n_hidden_;
n_outputs = n_outputs_;
addBOption("inputs to outputs", &inputs_to_outputs, false, "connections from inputs to outputs");
addROption("weight decay", &weight_decay, 0, "weight decay");
addBOption("softmax outputs", &is_softmax_outputs, false, "softmax outputs");
addBOption("sigmoid outputs", &is_sigmoid_outputs, false, "sigmoid outputs");
addBOption("log-softmax outputs", &is_log_softmax_outputs, false, "log-softmax outputs");
addBOption("tanh outputs", &is_tanh_outputs, false, "tanh outputs");
addBOption("sparse inputs", &is_sparse_inputs, false, "sparse inputs");
}
void MLP::init()
{
if(inputs_to_outputs && is_sparse_inputs)
error("MLP: sorry, connections from inputs to outputs and sparse inputs aren't compatible");
add_layer = NULL;
sum_layer = NULL;
if (n_hidden>0) {
if(is_sparse_inputs)
{
sparse_hidden_layer = new SparseLinear(n_inputs,n_hidden);
sparse_hidden_layer->setROption("weight decay",weight_decay);
sparse_hidden_layer->init();
addMachine(sparse_hidden_layer);
}
else
{
hidden_layer = new Linear(n_inputs,n_hidden);
hidden_layer->setROption("weight decay",weight_decay);
hidden_layer->init();
addMachine(hidden_layer);
}
if (inputs_to_outputs) {
add_layer = new Linear(n_inputs,n_outputs);
add_layer->setROption("weight decay",weight_decay);
add_layer->init();
addMachine(add_layer);
}
hidden_tanh_layer = new Tanh(n_hidden);
hidden_tanh_layer->init();
addLayer();
addMachine(hidden_tanh_layer);
if(is_sparse_inputs)
connectOn(sparse_hidden_layer);
else
connectOn(hidden_layer);
addLayer();
outputs_layer = new Linear(n_hidden,n_outputs);
outputs_layer->setROption("weight decay",weight_decay);
outputs_layer->init();
addMachine(outputs_layer);
connectOn(hidden_tanh_layer);
if (inputs_to_outputs) {
sum_layer = new SumMachine(n_outputs,2);
sum_layer->init();
addLayer();
addMachine(sum_layer);
connectOn(add_layer);
connectOn(outputs_layer);
}
} else {
if(is_sparse_inputs)
{
sparse_outputs_layer = new SparseLinear(n_inputs,n_outputs);
sparse_outputs_layer->setROption("weight decay",weight_decay);
sparse_outputs_layer->init();
addFCL(sparse_outputs_layer);
}
else
{
outputs_layer = new Linear(n_inputs,n_outputs);
outputs_layer->setROption("weight decay",weight_decay);
outputs_layer->init();
addFCL(outputs_layer);
}
}
if(is_softmax_outputs)
{
outputs_softmax_layer = new Softmax(n_outputs);
outputs_softmax_layer->init();
addFCL(outputs_softmax_layer);
}
else
{
if(is_sigmoid_outputs)
{
outputs_sigmoid_layer = new Sigmoid(n_outputs);
outputs_sigmoid_layer->init();
addFCL(outputs_sigmoid_layer);
}
else
{
if(is_log_softmax_outputs)
{
outputs_log_softmax_layer = new LogSoftmax(n_outputs);
outputs_log_softmax_layer->init();
addFCL(outputs_log_softmax_layer);
}
else
{
if (is_tanh_outputs)
{
outputs_tanh_layer = new Tanh(n_outputs);
outputs_tanh_layer->init();
addFCL(outputs_tanh_layer);
}
}
}
}
ConnectedMachine::init();
}
MLP::~MLP()
{
if (n_hidden>0) {
if(is_sparse_inputs)
delete sparse_hidden_layer;
else
delete hidden_layer;
delete hidden_tanh_layer;
delete outputs_layer;
}
else
{
if(is_sparse_inputs)
delete sparse_outputs_layer;
else
delete outputs_layer;
}
if(is_softmax_outputs)
delete outputs_softmax_layer;
else
{
if(is_sigmoid_outputs)
delete outputs_sigmoid_layer;
else
{
if(is_log_softmax_outputs)
delete outputs_log_softmax_layer;
else
{
if(is_tanh_outputs)
delete outputs_tanh_layer;
}
}
}
if (add_layer)
delete add_layer;
if (inputs_to_outputs)
delete sum_layer;
}
}
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